Overview

Dataset statistics

Number of variables12
Number of observations3013
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory282.6 KiB
Average record size in memory96.0 B

Variable types

NUM9
CAT2
BOOL1

Warnings

df_index has unique values Unique
Player has unique values Unique

Reproduction

Analysis started2021-01-08 02:29:18.926944
Analysis finished2021-01-08 02:29:30.271959
Duration11.35 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct3013
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3469.127116
Minimum2
Maximum6820
Zeros0
Zeros (%)0.0%
Memory size23.5 KiB
2021-01-08T10:29:30.344305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile388.2
Q11950
median3561
Q35016
95-th percentile6363.2
Maximum6820
Range6818
Interquartile range (IQR)3066

Descriptive statistics

Standard deviation1864.825383
Coefficient of variation (CV)0.5375488764
Kurtosis-1.066624468
Mean3469.127116
Median Absolute Deviation (MAD)1544
Skewness-0.07563128932
Sum10452480
Variance3477573.709
MonotocityStrictly increasing
2021-01-08T10:29:30.465181image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
67191< 0.1%
 
62761< 0.1%
 
13661< 0.1%
 
65181< 0.1%
 
13621< 0.1%
 
34091< 0.1%
 
13601< 0.1%
 
13561< 0.1%
 
34031< 0.1%
 
13541< 0.1%
 
Other values (3003)300399.7%
 
ValueCountFrequency (%) 
21< 0.1%
 
71< 0.1%
 
91< 0.1%
 
111< 0.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
68201< 0.1%
 
68181< 0.1%
 
68171< 0.1%
 
68151< 0.1%
 
68131< 0.1%
 

Player
Categorical

UNIQUE

Distinct3013
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
Kenny Young
 
1
Antone Exum
 
1
Igor Olshansky
 
1
Javon Hargrave
 
1
Dante' Hall
 
1
Other values (3008)
3008 
ValueCountFrequency (%) 
Kenny Young1< 0.1%
 
Antone Exum1< 0.1%
 
Igor Olshansky1< 0.1%
 
Javon Hargrave1< 0.1%
 
Dante' Hall1< 0.1%
 
Mike Sims-Walker1< 0.1%
 
Beau Sandland1< 0.1%
 
Nick Vannett1< 0.1%
 
Wayne Hunter1< 0.1%
 
Chris Galippo1< 0.1%
 
Other values (3003)300399.7%
 
2021-01-08T10:29:30.624492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3013 ?
Unique (%)100.0%
2021-01-08T10:29:30.777475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length13
Mean length12.90939263
Min length7

Pos
Categorical

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
OL
659 
DB
590 
DL
584 
LB
393 
WR
301 
Other values (2)
486 
ValueCountFrequency (%) 
OL65921.9%
 
DB59019.6%
 
DL58419.4%
 
LB39313.0%
 
WR30110.0%
 
RB2688.9%
 
TE2187.2%
 
2021-01-08T10:29:30.911425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-08T10:29:30.989405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:31.129375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Ht
Real number (ℝ≥0)

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.03916362
Minimum65
Maximum81
Zeros0
Zeros (%)0.0%
Memory size23.5 KiB
2021-01-08T10:29:31.219347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile69
Q172
median74
Q376
95-th percentile78
Maximum81
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.696917889
Coefficient of variation (CV)0.03642555854
Kurtosis-0.4436535815
Mean74.03916362
Median Absolute Deviation (MAD)2
Skewness-0.2750207079
Sum223080
Variance7.2733661
MonotocityNot monotonic
2021-01-08T10:29:31.311482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
7644214.7%
 
7543914.6%
 
7336011.9%
 
7435111.6%
 
7730310.1%
 
722879.5%
 
712317.7%
 
701735.7%
 
781585.2%
 
691083.6%
 
Other values (7)1615.3%
 
ValueCountFrequency (%) 
651< 0.1%
 
6640.1%
 
67160.5%
 
68421.4%
 
691083.6%
 
ValueCountFrequency (%) 
8120.1%
 
80240.8%
 
79722.4%
 
781585.2%
 
7730310.1%
 

Wt
Real number (ℝ≥0)

Distinct189
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.0375041
Minimum166
Maximum370
Zeros0
Zeros (%)0.0%
Memory size23.5 KiB
2021-01-08T10:29:31.427409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum166
5-th percentile189
Q1210
median247
Q3300
95-th percentile323
Maximum370
Range204
Interquartile range (IQR)90

Descriptive statistics

Standard deviation46.34712537
Coefficient of variation (CV)0.1838897966
Kurtosis-1.257092653
Mean252.0375041
Median Absolute Deviation (MAD)43
Skewness0.2037557047
Sum759389
Variance2148.05603
MonotocityNot monotonic
2021-01-08T10:29:31.558629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
309441.5%
 
210401.3%
 
303391.3%
 
307391.3%
 
204381.3%
 
211371.2%
 
209371.2%
 
202371.2%
 
306361.2%
 
300351.2%
 
Other values (179)263187.3%
 
ValueCountFrequency (%) 
1661< 0.1%
 
1681< 0.1%
 
16920.1%
 
1701< 0.1%
 
1711< 0.1%
 
ValueCountFrequency (%) 
3701< 0.1%
 
3691< 0.1%
 
3661< 0.1%
 
3581< 0.1%
 
35530.1%
 

Forty
Real number (ℝ≥0)

Distinct148
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.818927979
Minimum4.26
Maximum9.99
Zeros0
Zeros (%)0.0%
Memory size23.5 KiB
2021-01-08T10:29:31.692403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.26
5-th percentile4.41
Q14.55
median4.72
Q35.06
95-th percentile5.4
Maximum9.99
Range5.73
Interquartile range (IQR)0.51

Descriptive statistics

Standard deviation0.395987359
Coefficient of variation (CV)0.08217332999
Kurtosis56.06475599
Mean4.818927979
Median Absolute Deviation (MAD)0.22
Skewness4.730393578
Sum14519.43
Variance0.1568059885
MonotocityNot monotonic
2021-01-08T10:29:31.810409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.5752.5%
 
4.65622.1%
 
4.6612.0%
 
4.62612.0%
 
4.56581.9%
 
4.53571.9%
 
4.59551.8%
 
4.51491.6%
 
4.58491.6%
 
4.52471.6%
 
Other values (138)243980.9%
 
ValueCountFrequency (%) 
4.261< 0.1%
 
4.2830.1%
 
4.2930.1%
 
4.330.1%
 
4.3190.3%
 
ValueCountFrequency (%) 
9.9960.2%
 
61< 0.1%
 
5.861< 0.1%
 
5.851< 0.1%
 
5.821< 0.1%
 

Vertical
Real number (ℝ≥0)

Distinct52
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.63923
Minimum19.5
Maximum45.5
Zeros0
Zeros (%)0.0%
Memory size23.5 KiB
2021-01-08T10:29:31.941265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum19.5
5-th percentile25
Q129.5
median33
Q335.5
95-th percentile39.5
Maximum45.5
Range26
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.287588368
Coefficient of variation (CV)0.1313630367
Kurtosis-0.3416012776
Mean32.63923
Median Absolute Deviation (MAD)3
Skewness-0.1768741473
Sum98342
Variance18.38341402
MonotocityNot monotonic
2021-01-08T10:29:32.085429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
341464.8%
 
33.51454.8%
 
351414.7%
 
35.51404.6%
 
331394.6%
 
34.51364.5%
 
321334.4%
 
361334.4%
 
32.51163.8%
 
301053.5%
 
Other values (42)167955.7%
 
ValueCountFrequency (%) 
19.51< 0.1%
 
201< 0.1%
 
20.520.1%
 
2140.1%
 
21.540.1%
 
ValueCountFrequency (%) 
45.51< 0.1%
 
4530.1%
 
441< 0.1%
 
43.540.1%
 
4330.1%
 

BenchReps
Real number (ℝ≥0)

Distinct44
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.9724527
Minimum2
Maximum45
Zeros0
Zeros (%)0.0%
Memory size23.5 KiB
2021-01-08T10:29:32.227458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q116
median21
Q325
95-th percentile32
Maximum45
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.37435291
Coefficient of variation (CV)0.3039393151
Kurtosis-0.03191149246
Mean20.9724527
Median Absolute Deviation (MAD)4
Skewness0.1986224611
Sum63190
Variance40.63237502
MonotocityNot monotonic
2021-01-08T10:29:32.343463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%) 
191936.4%
 
231836.1%
 
211816.0%
 
241765.8%
 
201715.7%
 
221715.7%
 
171705.6%
 
181565.2%
 
151484.9%
 
261474.9%
 
Other values (34)131743.7%
 
ValueCountFrequency (%) 
21< 0.1%
 
31< 0.1%
 
450.2%
 
520.1%
 
670.2%
 
ValueCountFrequency (%) 
4520.1%
 
441< 0.1%
 
431< 0.1%
 
4230.1%
 
4120.1%
 

BroadJump
Real number (ℝ≥0)

Distinct57
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.4769333
Minimum82
Maximum140
Zeros0
Zeros (%)0.0%
Memory size23.5 KiB
2021-01-08T10:29:32.461550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile96
Q1107
median115
Q3120
95-th percentile128
Maximum140
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.587640874
Coefficient of variation (CV)0.08448977775
Kurtosis-0.3279653551
Mean113.4769333
Median Absolute Deviation (MAD)6
Skewness-0.3628783329
Sum341906
Variance91.92285752
MonotocityNot monotonic
2021-01-08T10:29:32.597448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1181484.9%
 
1211404.6%
 
1201374.5%
 
1151374.5%
 
1171324.4%
 
1161183.9%
 
1131163.8%
 
1141153.8%
 
1191123.7%
 
1121033.4%
 
Other values (47)175558.2%
 
ValueCountFrequency (%) 
8220.1%
 
851< 0.1%
 
861< 0.1%
 
871< 0.1%
 
8840.1%
 
ValueCountFrequency (%) 
1401< 0.1%
 
13920.1%
 
1381< 0.1%
 
1371< 0.1%
 
13630.1%
 

Cone
Real number (ℝ≥0)

Distinct274
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.194829074
Minimum3.97
Maximum9.04
Zeros0
Zeros (%)0.0%
Memory size23.5 KiB
2021-01-08T10:29:32.736470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.97
5-th percentile6.6
Q16.95
median7.19
Q37.58
95-th percentile8.1
Maximum9.04
Range5.07
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation0.7059513273
Coefficient of variation (CV)0.09811926316
Kurtosis7.664803669
Mean7.194829074
Median Absolute Deviation (MAD)0.29
Skewness-2.152367367
Sum21678.02
Variance0.4983672766
MonotocityNot monotonic
2021-01-08T10:29:32.861501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7.09531.8%
 
7.07471.6%
 
7.08451.5%
 
7451.5%
 
6.9441.5%
 
6.96391.3%
 
6.94381.3%
 
7.15371.2%
 
6.97361.2%
 
7.12361.2%
 
Other values (264)259386.1%
 
ValueCountFrequency (%) 
3.971< 0.1%
 
3.991< 0.1%
 
4.031< 0.1%
 
4.041< 0.1%
 
4.071< 0.1%
 
ValueCountFrequency (%) 
9.041< 0.1%
 
91< 0.1%
 
8.841< 0.1%
 
8.781< 0.1%
 
8.7220.1%
 

Shuttle
Real number (ℝ≥0)

Distinct218
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.519064056
Minimum3.75
Maximum8.28
Zeros0
Zeros (%)0.0%
Memory size23.5 KiB
2021-01-08T10:29:32.996650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.75
5-th percentile4.04
Q14.21
median4.38
Q34.63
95-th percentile5.07
Maximum8.28
Range4.53
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.6098972767
Coefficient of variation (CV)0.1349609718
Kurtosis14.22807791
Mean4.519064056
Median Absolute Deviation (MAD)0.2
Skewness3.553528472
Sum13615.94
Variance0.3719746881
MonotocityNot monotonic
2021-01-08T10:29:33.118940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.4692.3%
 
4.28692.3%
 
4.2622.1%
 
4.21612.0%
 
4.25612.0%
 
4.15602.0%
 
4.07551.8%
 
4.18541.8%
 
4.32521.7%
 
4.37501.7%
 
Other values (208)242080.3%
 
ValueCountFrequency (%) 
3.751< 0.1%
 
3.81< 0.1%
 
3.8120.1%
 
3.8230.1%
 
3.831< 0.1%
 
ValueCountFrequency (%) 
8.281< 0.1%
 
8.151< 0.1%
 
8.131< 0.1%
 
8.061< 0.1%
 
8.021< 0.1%
 

pro bowl?
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
0
2753 
1
 
260
ValueCountFrequency (%) 
0275391.4%
 
12608.6%
 
2021-01-08T10:29:33.227507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2021-01-08T10:29:19.399965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:19.527979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:20.122241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:20.250926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:20.371468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:20.485048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:20.596749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:20.715432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:20.831122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:20.942823image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:21.065497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:21.187170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:21.312834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:21.439495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:21.560013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:21.684019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:21.800997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:21.913986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:22.024980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:22.156286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:22.283303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:22.410003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:22.531327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:22.654265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:22.782382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:22.904324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:23.025386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:23.140332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:23.261053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:23.378311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:23.506052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:23.621401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:23.729383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:23.837358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:23.954306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:24.061319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:24.174403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:24.303292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:24.429393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:24.551365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:24.671011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:24.797670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:24.914358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:25.041020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:25.156743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:25.262429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:25.394077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:25.528380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:25.647369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:25.754352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:25.860328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:25.961335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:26.075462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:26.180411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:26.287373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:26.435366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:26.561411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:26.706405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:26.823438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:26.938384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:27.046402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:27.161367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:27.277401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:27.385430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:27.499375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:27.611376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:27.735423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:27.844396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:27.960328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:28.076445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:28.194414image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:28.318405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:28.448363image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:28.575311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:28.692995image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:28.825642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:28.934318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:29.055028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:29.156758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:29.584474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:29.689195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-08T10:29:33.295974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-08T10:29:33.477501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-08T10:29:33.686463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-08T10:29:33.878518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-01-08T10:29:29.890655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-08T10:29:30.170540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

df_indexPlayerPosHtWtFortyVerticalBenchRepsBroadJumpConeShuttlepro bowl?
02Darnell AlfordOL763345.5625.023.094.08.484.980
17Corey AtkinsLB722374.7231.021.0112.07.964.390
29Reggie AustinDB691754.4435.017.0119.07.034.140
311Mark BaniewiczOL783125.3428.020.096.07.724.730
412Rashidi BarnesDB722084.6235.010.0114.06.924.320
513David BarrettDB701994.4437.516.0116.06.814.040
620Rogers BeckettDB732074.6239.515.0119.06.484.290
721Brad BedellOL763025.0731.517.0103.07.764.580
822Marcus Bell-01LB742374.7831.521.0111.07.174.330
924Michael BoireauDL762745.0929.026.0105.07.684.490

Last rows

df_indexPlayerPosHtWtFortyVerticalBenchRepsBroadJumpConeShuttlepro bowl?
30036804Evan WeaverLB742354.7632.015.0117.07.024.210
30046808Darryl WilliamsOL753105.2325.523.0102.07.884.760
30056809Raequan WilliamsDL763035.0425.517.0101.07.724.780
30066811Isaiah WilsonOL793405.3229.026.0110.08.265.070
30076812Logan WilsonLB742414.6332.021.0121.07.074.270
30086813Rob WindsorDL762854.9028.521.0111.07.474.440
30096815Tristan WirfsOL773224.8536.524.0121.07.654.680
30106817Charlie WoernerTE772454.7834.521.0120.07.184.460
30116818D.J. WonnumDL772544.7334.520.0123.07.254.440
30126820David WoodwardLB742354.7933.516.0114.07.344.370